Abstract: Systems and techniques for location management are described herein. In an example, a system may include at least one processor and at least one memory with instructions stored thereon that when executed by the processor, cause the processor to obtain data originating from one or more sensors proximate to the location. A trained activity-based detection model may identify an activity at the location and perform a determination of a service to be offered at the location based on the detected activity. The system may then send a message to a user offering the service to the user, and in response to receiving an authorization accepting the service from the user, cause the service to be implemented at the location, which may include classifying the service as a service type, matching the service type to a service provider, and sending a notification to the service provider.

Abstract: Various methods, systems, and use cases for a stable and automated transformation of a networked computing system are provided, to enable a transformation to the configuration of the computing system (e.g., software or firmware upgrade, hardware change, etc.). In an example, automated operations include: identifying a transformation to apply to a configuration of the computing system, for a transformation that affects a network service provided by the computing system; identifying operational conditions used to evaluate results of the transformation; attempting to apply the transformation, using a series of stages that have rollback positions when the identified operational conditions are not satisfied; and determining a successful or unsuccessful result of the attempt to apply the transformation. For an unsuccessful result, remediation may be performed to the configuration, with use of one or more rollback positions; for a successful result, a new restore state is established from the completion state.

Abstract: Various aspects of methods, systems, and use cases for multi-entity (e.g., multi-tenant) edge computing deployments are disclosed. Among other examples, various configurations and features enable the management of resources (e.g., controlling and orchestrating hardware, acceleration, network, processing resource usage), security (e.g., secure execution and communication, isolation, conflicts), and service management (e.g., orchestration, connectivity, workload coordination), in edge computing deployments, such as by a plurality of edge nodes of an edge computing environment configured for executing workloads from among multiple tenants.

Abstract: Techniques for block based performance monitoring are described. In an embodiment, an apparatus includes execution hardware to execute a plurality of instructions; and block-based sampling hardware. The block-based sampling hardware is to identify, based on a first branch instruction of the plurality of instructions and a second branch instruction of the plurality of instructions, a block of instructions; and to collect, during execution of the block of instructions, performance information.

Abstract: An architecture to perform resource management among multiple network nodes and associated resources is disclosed. Example resource management techniques include those relating to: proactive reservation of edge computing resources; deadline-driven resource allocation; speculative edge QOS pre-allocation; and automatic QoS migration across edge computing nodes.

Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic Application Programming Interface (API) contract generation and conversion. In an example, a first sidecar of a source microservice intercepts a first call to a first API exposed by a destination microservice. The first call makes use of a first API technology specified by a first contract and is originated by the source microservice. An API technology is selected from multiple API technologies. The selected API technology is determined to be different than the first API technology. Based on the first contract, a second contract is dynamically generated that specifies an intermediate API that makes use of the selected API technology. A second sidecar of the destination microservice is caused to generate the intermediate API and connect the intermediate API to the first API.

Abstract: An apparatus and system to provide separate network slices for security events are described. A dedicated secure network slice is provided for PDP data from a UE. The network slice is used for detecting security issues and sending security-related information to clients. The communications in the dedicated network slice are associated with a special PDP context used by the UE to interface with the network slice. Once the UE has detected a security issue or has been notified of the security issue on the network or remote servers, the UE uses a special PDP service, and is able to stop uplink/downlink channels, close running applications and enter into a safe mode, cut off connections to the networks, and try to determine alternate available connectivity.

Abstract: Systems and techniques for intelligent data forwarding in edge networks are described herein. A request may be received from an edge user device for a service via a first endpoint. A time value may be calculated using a timestamp of the request. Motion characteristics may be determined for the edge user device using the time value. A response to the request may be transmitted to a second endpoint based on the motion characteristics.

Abstract: A mechanism is described for facilitating misuse index for explainable artificial intelligence in computing environments, according to one embodiment. A method of embodiments, as described herein, includes mapping training data with inference uses in a machine learning environment, where the training data is used for training a machine learning model. The method may further include detecting, based on one or more policy/parameter thresholds, one or more discrepancies between the training data and the inference uses, classifying the one or more discrepancies as one or more misuses, and creating a misuse index listing the one or more misuses.

Abstract: Various systems and methods of initiating and performing contextualized AI inferencing, are described herein. In an example, operations performed with a gateway computing device to invoke an inferencing model include receiving and processing a request for an inferencing operation, selecting an implementation of the inferencing model on a remote service based on a model specification and contextual data from the edge device, and executing the selected implementation of the inferencing model, such that results from the inferencing model are provided back to the edge device. Also in an example, operations performed with an edge computing device to request an inferencing model include collecting contextual data, generating an inferencing request, transmitting the inference request to a gateway device, and receiving and processing the results of execution. Further techniques for implementing a registration of the inference model, and invoking particular variants of an inference model, are also described.

Abstract: An apparatus and method for closed loop dynamic resource allocation.

Abstract: Various aspects of methods, systems, and use cases for multi-entity (e.g., multi-tenant) edge computing deployments are disclosed. Among other examples, various configurations and features enable the management of resources (e.g., controlling and orchestrating hardware, acceleration, network, processing resource usage), security (e.g., secure execution and communication, isolation, conflicts), and service management (e.g., orchestration, connectivity, workload coordination), in edge computing deployments, such as by a plurality of edge nodes of an edge computing environment configured for executing workloads from among multiple tenants.

Abstract: In one embodiment, a system includes a device and a host. The device includes a device stream buffer. The host includes a processor to execute at least a first application and a second application, a host stream buffer, and a host scheduler. The first application is associated with a first transmit streaming channel to stream first data from the first application to the device stream buffer. The first transmit streaming channel has a first allocated amount of buffer space in the device stream buffer. The host scheduler schedules enqueue of the first data from the first application to the first transmit streaming channel based at least in part on availability of space in the first allocated amount of buffer space in the device stream buffer. Other embodiments are described and claimed.

Abstract: A computing node includes a NIC and processing circuitry configured to select a subset of computing resources from a set of available computing resources to initiate a parameter sweep associated with a parameter sweep request received. A plurality of settings is applied to each computing resource of the subset to generate a plurality of resource mappings during the parameter sweep. Each resource mapping of the plurality of resource mappings indicates at least one computing resource of the subset and a corresponding at least one setting of the plurality of settings. Telemetry information for the subset of computing resources is retrieved, the telemetry information is generated during the parameter sweep. A resource mapping of the plurality of resource mappings is selected based on a comparison of the telemetry information with an SLO. A reconfiguration of the available computing resources is performed based on the selected resource mapping.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for workload placement in an edge environment. An example apparatus for workload placement in an edge environment includes an orchestrator to receive a request to execute a workload from an edge platform within an edge environment, and a capability controller to analyze the request to determine operating parameters for the workload from the edge platform, and analyze candidate edge tier and edge platform placements based on the operating parameters, the orchestrator to determine a candidate edge tier and edge platform placement for the workload based on a candidate edge tier and edge platform placement that satisfies the operating parameters.

Abstract: Methods, systems, and use cases for orchestrator execution planning using a distributed ledger are discussed, including an orchestration system with memory and at least one processing circuitry coupled to the memory. The processing circuitry is configured to perform operations to generate an execution plan for a workload based on an SLA. The execution plan includes state transitions associated with corresponding edge service instances. A distributed ledger record is retrieved from the ledger based on a reinforcement learning reward value specified by the record. The reward value is associated with a state transition of the plurality of state transitions. An edge node is selected based on the retrieved distributed ledger record. Execution of an edge service instance of the plurality of edge service instances by the edge node is scheduled. The execution of the edge service instance corresponds to the state transition associated with the reinforcement learning reward value.

Abstract: An architecture to perform resource management among multiple network nodes and associated resources is disclosed. Example resource management techniques include those relating to: proactive reservation of edge computing resources; deadline-driven resource allocation; speculative edge QoS pre-allocation; and automatic QoS migration across edge computing nodes.

Abstract: Methods, systems, and use cases for geofence-based edge service control and authentication are discussed, including an orchestration system with memory and at least one processing circuitry coupled to the memory. The processing circuitry is configured to perform operations to obtain, from a plurality of connectivity nodes providing edge services, physical location information, and resource availability information associated with each of the plurality of connectivity nodes. An edge-to-edge location graph (ELG) is generated based on the physical location information and the resource availability information, the ELG indicating a subset of the plurality of connectivity nodes that are available for executing a plurality of services associated with an edge workload. The connectivity nodes are provisioned with the ELG and a workflow execution plan to execute the plurality of services, the workflow execution plan including metadata with a geofence policy.

Abstract: In function-as-a-service (FaaS) environments, a client makes use of a function executing within a trusted execution environment (TEE) on a FaaS server. Multiple tenants of the FaaS platform may provide functions to be executed by the FaaS platform via a gateway. Each tenant may provide code and data for any number of functions to be executed within any number of TEEs on the FaaS platform and accessed via the gateway. Additionally, each tenant may provide code and data for a single surrogate attester TEE. The client devices of the tenant use the surrogate attester TEE to attest each of the other TEEs of the tenant and establish trust with the functions in those TEEs. Once the functions have been attested, the client devices have confidence that the other TEEs of the tenant are running on the same platform as the gateway.

Abstract: Example methods, apparatus, and systems to manage quality of service with respect to service level agreements in a computing device are disclosed. An example apparatus includes a first mesh proxy assigned to a first platform-agnostic application, the first mesh proxy to generate a first resource request signal based on a first service level agreement requirement from the first platform-agnostic application; a second mesh proxy assigned to a second platform-agnostic application, the second mesh proxy to generate a second resource request signal based on a second service level agreement requirement from second platform-agnostic application; and a load balancer to allocate hardware resources for the first platform-agnostic application and the second platform-agnostic application based on the first resource request signal and the second resource request signal.